III: Small: Collaborative Research: Harnessing Big Data for Improving Career Mobility

美国大学生在职业发展和工作流动性方面面临严峻挑战，这对他们的长期职业成功至关重要，尤其是在全球大流行时期。事实上，经常困扰学生的问题包括下一步选择什么样的职业，如何为未来的新工作更新技能，以及应该抓住哪些学习机会。在许多大学中，不同专业和社会经济地位的不同群体的学生中，这些挑战越来越多地被观察到。该项目收集和分析学术课程和学生职业数据，发现大学课程和学生职业发展的有用模式，研究学生的职业选择，并制定完善的解决方案，以提高他们的职业流动性。本研究对数据挖掘、机器学习、教育和职业数据分析等领域做出了重大贡献。本项目的研究成果可以为理解和提高大学生的职业成功提供新的途径，为大学生的职业发展决策提供有用的见解和工具，增强高校就业和学业指导办公室的服务能力。该项目通过开发新的课程模块，将研究与教育结合起来，让研究生和本科生参与研究，并为当地K-12学生提供研究展示。本项目侧重于以下三个具体目标（SA）：挖掘和提供有关大学课程和毕业生职业发展的有用语义和模式；大学毕业生的职业选择研究并制定复杂的解决方案，以提高大学毕业生的职业流动性。为了实现第一个SA，该项目开发了一种新的上下文感知深度学习方法，用于从异构文本数据中挖掘语义，并发现富有洞察力的水平和垂直模式。为了解决第二种问题，本项目挖掘了多尺度职业路径模式，并开发了一种新的层次神经网络方法来建模和预测毕业生的职业选择。为了实现第三个SA，本项目开发了新的强化学习方法，为研究生和在校生推荐学习项目。该项目的成果将以同行评议出版物、公开数据集、教程、研讨会和讲习班的形式传播。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

U.S. college students are facing critical challenges for their career development and job mobility, which is vital for their long-term career success, especially during global pandemic times. Indeed, the questions that often puzzle students include what career choices to choose next, how to update skills for future new jobs, and which learning opportunities to take. These challenges have been increasingly observed among different groups of students in different majors and socioeconomic statuses at many universities. This project collects and analyzes academic curriculum and student career data, discovers useful patterns about college curriculum and students’ career development, studies students’ career choices, and develops sophisticated solutions to improve their career mobility. This study makes significant contributions to the fields of data mining, machine learning, and education and career data analytics. The results of this project can bring new ways for understanding and improving college graduates’ career success, provide useful insights and tools for students to make their decisions on career development, and augment the service capability of college career and academic advising offices. This project integrates the research with education through new course module development, involving graduate and undergraduate students in research, and research showcases for local K-12 students. This project focuses on the following three specific aims (SA): mining and informing useful semantics and patterns about college curriculum and graduates’ career development; studying the career choices of college graduates; and developing sophisticated solutions to improve career mobility of college graduates. To achieve the first SA, this project develops a novel context-aware deep learning method for mining semantics from heterogenous textual data and discovers insightful horizontal and vertical patterns. To solve the second SA, this project mines multiple-scale career path patterns and develops a new hierarchical neural network method to model and predict graduates’ career choices. To achieve the third SA, this project develops novel reinforcement learning methods to recommend learning items for both graduated students and enrolled ones. The results of this project will be disseminated in the form of peer-reviewed publications, publicly available data set, tutorials, seminars, and workshops.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.